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      Pharmacogenomics applied to recombinant human growth hormone responses in children with short stature

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          Abstract

          We present current knowledge concerning the pharmacogenomics of growth hormone therapy in children with short stature. We consider the evidence now emerging for the polygenic nature of response to recombinant human growth hormone (r-hGH). These data are related predominantly to the use of transcriptomic data for prediction. The impact of the complex interactions of developmental phenotype over childhood on response to r-hGH are discussed. Finally, the issues that need to be addressed in order to develop a clinical test are described.

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          RNA velocity of single cells

          RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
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            Generalizing RNA velocity to transient cell states through dynamical modeling

            RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.
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              An Expanded View of Complex Traits: From Polygenic to Omnigenic

              A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.
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                Author and article information

                Contributors
                adam.stevens@manchester.ac.uk
                reena.perchard@manchester.ac.uk
                terence.garner@manchester.ac.uk
                peter.clayton@manchester.ac.uk
                philip.murray@mft.nhs.uk
                Journal
                Rev Endocr Metab Disord
                Rev Endocr Metab Disord
                Reviews in Endocrine & Metabolic Disorders
                Springer US (New York )
                1389-9155
                1573-2606
                12 March 2021
                12 March 2021
                2021
                : 22
                : 1
                : 135-143
                Affiliations
                GRID grid.5379.8, ISNI 0000000121662407, Division of Developmental Biology and Medicine, School of Medical Sciences, The Faculty of Biology, Medicine, and Health, , University of Manchester, ; Manchester, UK
                Author information
                http://orcid.org/0000-0002-1950-7325
                http://orcid.org/0000-0002-3072-0055
                http://orcid.org/0000-0003-3962-9730
                http://orcid.org/0000-0003-1225-4537
                http://orcid.org/0000-0002-1480-1576
                Article
                9637
                10.1007/s11154-021-09637-1
                7979669
                33712998
                fefff22e-a545-40bb-bf62-04eb7a796b29
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 February 2021
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                © Springer Science+Business Media, LLC, part of Springer Nature 2021

                Endocrinology & Diabetes
                pharmacogenomics,transcriptomics,growth hormone,interactome
                Endocrinology & Diabetes
                pharmacogenomics, transcriptomics, growth hormone, interactome

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